general-image-detector-detic_clipR50Caption-coco
--
1
Sign up or Log in to view model predictions.
Notes
Detecting Twenty-thousand Classes using Image-level Supervision
Detic: A Detector with image classes that can use image-level labels to easily train detectors.
Detecting Twenty-thousand Classes using Image-level Supervision,
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra,
ECCV 2022 (arXiv 2201.02605)
Features
-
Detects any class given class names (using CLIP).
-
We train the detector on ImageNet-21K dataset with 21K classes.
-
Cross-dataset generalization to OpenImages and Objects365 without finetuning.
-
State-of-the-art results on Open-vocabulary LVIS and Open-vocabulary COCO.
Detic_CLIP_R50_1x_caption-image Performance
Open-vocabulary COCO
| Name | Training time | box mAP50 | box mAP50_novel |
|---|---|---|---|
| BoxSup_CLIP_R50_1x | 12h | 39.3 | 1.3 |
| Detic_CLIP_R50_1x_image | 13h | 44.7 | 24.1 |
| Detic_CLIP_R50_1x_caption | 16h | 43.8 | 21.0 |
| Detic_CLIP_R50_1x_caption-image | 16h | 45.0 | 27.8 |
Note
-
All models are trained with ResNet50-C4 without multi-scale augmentation. All models use CLIP embeddings as the classifier.
-
We extract class names from COCO-captions as image-labels.
Detic_CLIP_R50_1x_imageuses the max-size loss;Detic_CLIP_R50_1x_captiondirectly uses CLIP caption embedding within each mini-batch for classification;Detic_CLIP_R50_1x_caption-imageuses both losses. -
We report box mAP50 under the "generalized" open-vocabulary setting.